The present invention relates to a signal evaluation apparatus and signal evaluation method for evaluating a reproduction signal of a recording medium.
In recent years, according to the digitization of various sorts of information including image information and audio information, the amount of digital information has been rapidly increased. In accordance with this, optical disks and optical disk apparatuses suitable for increase in capacity and increase in density have been developed. With the development in density increase of the optical disks, reproduction signals read from the optical disks have been degraded in quality, and the evaluation of reproduction signals is important. The evaluation of reproduction signals as described above is used for quality assurance in the shipment stage of the optical disks or used for adjusting each part of optical disk apparatuses so that the reproduction signal has the best quality.
Conventionally, jitter, bit error rate (BER) and the like have been used for the evaluation of optical disks or optical disk apparatuses, and Japanese Patent Laid-Open Publication No. HEI 10-21651 discloses a signal evaluation apparatus appropriate for PRML (Partial Response Maximum Likelihood), which has been used for the reproduction signal processing of optical disk apparatuses.
The above-mentioned signal evaluation apparatus will be described below with reference to the drawings.
Reference is first made to the case where a reproduction signal is decoded by the aforementioned signal evaluation apparatus according to the Viterbi decoding system. A (1,7) RLL code that has a minimum run length limited to one is adopted, and PR (1,2,1) is adopted as a PRML system. The relation between a record bit sequence bk and a state Sk at the time point of sample k (k=0,1,2,3) assumes four states S0, S1, S2 and S3 as shown in Table 1.
TABLE 1StateRecord BitSkbk−1bkS000S101S211S310
Each state transits to the next state according to the next record bit, and this state transition is called the “branch”.
Table 2 shows the relation between the record bit and the state transition, and the number of branches is six because the minimum run length is limited to one.
TABLE 2ExpectedRecord BitStateValueNo.bk−2bk−1bkSk−1SkYka000S0S0−1.0b100S3S0−0.5c001S0S1−0.5d011S1S20.5e111S2S21.0f110S2S30.5
According to PR (1,2,1), the reproduction signal level is determined by a 3-bit record bit sequence. Therefore, the expected value, i.e., the reproduction signal level with an ideal waveform free of noise is expressed as an expected value Yk in Table 2. In Table 2, the minimum value and the maximum value of the reproduction signal level with the ideal waveform are standardized to −1 and 1, respectively.
In this case, the branch metric (Zk−Yk)2 of each branch at the time point of sample k is calculated. Zk is the reproduction signal level at the time point of sample k. This “branch metric”, which is the square of the difference between the reproduction signal level and the expected value, therefore means the square error of the reproduction signal level with respect to the expected value. The branch metric is used for determining which branch is to be selected when two branches converge into a certain state. Then, a continuous series of branches is called the “path”, and a continuous series of selected branches is called the “survival path”.
Assuming that the cumulative value of the branch metrics for the survival path in each state at the time of sample k−1 is mk−1, then the sum of the value mk−1 and the branch metric bmk at the time point of sample k becomes the cumulative total value of the branch metrics at the time point of sample k.
As described above, since the branch metric means the square error, the cumulative total value of the branch metrics is the sum total of errors. Therefore, the branch of the smaller value of (mk−1+bmk) is selected.
For example, the branches whose states become S0 at the time point of sample k, are the two of the branch that transits from S0 to S0 and the branch that transits from S3 to S0 according to Table 2. It is postulated that the cumulative values of the branch metrics are m0k−1 and m3k−1 and the branch metrics are bmak and bmbk. Accordingly, assuming that the cumulative total values of the branch metrics at the time point of sample k are m0k(a) and m0k(b), respectively, then there hold the equations (1) and (2):M0k(a)=m0k−1+bmak  (1)m0k(b)=m3k−1+bmbk  (2)Further, m0k(a) and m0k(b) are compared in magnitude with each other, and the branch of the smaller one is selected.
In this case, if the correct state at the time point of sample k is S0 and the correct transition is a, then there is executed the calculation of the equation (3):Δmk=m0k(b)−m0k(a)  (3)and this Δmk is called the “difference metric”.
Moreover, if the correct state at the time point of sample k is S0 and the correct transition is b, then the difference metric Δmk is expressed by the equation (4):Δmk=m0k(a)−m0k(b)  (4)That is, the cumulative total value of the branch metrics of the correct transition is subtracted from the cumulative total value of the branch metrics of the erroneous transition. To know the correct state and the correct transition, Japanese Patent Laid-Open Publication No. HEI 10-21651 discloses a method for using the recorded data sequence and a method for delaying the reproduced data sequence when the error rate of the reproduced data sequence is low.
In this case, if the branch to be selected as a result of decoding is the correct branch, then the difference metric Δmk has a positive value. However, if an erroneous branch is selected, the difference metric has a negative value.
FIG. 3 shows the distribution of the difference metrics calculated at each sample time point. Postulating that the normal distribution has a mean value μ and a standard deviation σ on the assumption that the distribution profile can be approximated to the normal distribution, then the probability that the difference metric will become negative is equal to a bit error rate (BER) since the difference metric becomes negative in the case of an error as described hereinbefore. That is, by executing a calculation according to the equation (5):
                    BER        =                              1                                                            2                  ⁢                  π                                            ·              σ                                ⁢                                    ∫                              -                ∞                            0                        ⁢                                          ⅇ                                  -                                                                                    (                                                  t                          -                          μ                                                )                                            2                                                              2                      ⁢                                              σ                        2                                                                                                        ⁢                              ⅆ                t                                                                        (        5        )            the bit error rate BER can be estimated. Moreover, when it is only required to know the relative quality of the bit error rate BER of an optical disk or an optical disk apparatus instead of the absolute value of the bit error rate BER, it is acceptable to use σ/μ as an index.
FIG. 3 shows a distribution that has a single peak. However, when there is a limitation on the minimum run length, there is a distribution that has a plurality of peaks as shown in FIG. 4. Even in this case, assuming that the difference metric distribution conforms to the normal distribution in the region where the difference metric is smaller than the value expressed by the mean value μ shown in FIG. 4 paying attention only to the distribution that has a peak position located nearest to zero, then the bit error rate BER can be calculated similarly to the distribution that has a single peak. However, dissimilarly to the distribution that has a single peak, the mean value μ cannot be obtained from the arithmetic mean. Moreover, if the mean value μ is not obtained, then the standard deviation σ cannot be calculated.
In order to solve this problem, the aforementioned signal evaluation apparatus extracts only the sequence of which the difference metric comes to have the highest probability of becoming negative, i.e., the sequence that passes through the path that forms a distribution that has a peak position located nearest to zero (hereinafter referred to as a “minimum distribution”). In paths as described above, a distance between the two paths has a minimum value, and there are four paths according to this explanation. Table 3 shows four sequences that form the minimum distribution.
By executing this processing, the distribution that has a single peak as shown in FIG. 3 is obtained, and both the mean value μ and the standard deviation a can be calculated comparatively easily.
TABLE 3StateNo.Sk−3Sk−2Sk−1SkAS0S0S1S2BS0S1S2S2CS2S2S3S0DS2S3S0S0
As described above, by extracting only the sequence that passes through a prescribed path from the data sequence, the aforementioned signal evaluation apparatus can obtain the distribution that has a single peak.
However, the aforementioned signal evaluation apparatus extracts only part of the whole data sequence, and therefore, only the bit error rate of part of all the data can be calculated from the obtained distribution. In other words, there is a problem that the aforementioned signal evaluation apparatus cannot obtain the accurate bit error rate of the whole data sequence although the correct bit error rate is the ratio of the number of errors to the number of all the samples.
Since the minimum distribution has a peak position located nearest to zero, it can be considered that almost all the errors occur in the data sequence included in this distribution. Even though the number of generated errors is same, the bit error rate BER is varied when the number of all the samples is varied.
For example, assuming that the number of error is one and the number of samples included in the minimum distribution is 10000, then the error rate of the minimum distribution is 1×10−4. In this case, if the number of all the measurement samples is equal to the number of samples of the minimum distribution, then the bit error rate BER also becomes 1×10−4. However, if the number of all the samples is 100000, then the bit error rate BER is 1×10−5. As described above, even if the number of generated errors is same, the bit error rate BER is varied depending on the ratio of the number of samples included in the minimum distribution to the number of all the samples. The ratio of the number of samples included in the minimum distribution with respect to the number of all the samples varies according to the data pattern to be recorded, and the distribution profile of the difference metrics shown in FIG. 4 also varies.